Generalized multihypothesis motion compensated filter for grayscale and color video denoising

  • Authors:

  • Jingjing Dai;Oscar C. Au;Feng Zou;Chao Pang

  • Affiliations:

  • Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

  • Venue:
  • Signal Processing
  • Year:
  • 2013

Quantified Score

Hi-index 0.08

Visualization

Abstract

This work deals with the additive white Gaussian noise reduction in grayscale and color video sequences. The first main contribution of this paper is the generalized multihypothesis motion compensated filter (GMHMCF) which combines the merits of the traditional time-recursive filter and non-recursive filter in the sense that the reference frame buffer of GMHMCF consists of the denoised previous frames as well as the noisy future frames, such that both the backward and the forward inter-frame correlation can be well exploited. To establish the temporal correspondence between neighboring frames, GMHMCF employs a noise-robust motion estimation (ME) with a pre-defined motion vector (MV) regularization term to construct multiple temporal predictions (hypotheses), which are combined with the current noisy observation through a linear optimal estimator to restore the noise-free signal. The denoising performance of different reference frame configurations is analytically discussed and experimentally tested. Another important contribution of this paper is to extend the GMHMCF to color noise reduction. We examine the primary factors that affect the denoising error of the linear estimator and derive an adaptive optimal luminance-chrominance space such that, when the RGB samples are converted to that new space, GMHMCF can be applied to the individual color components to achieve the minimum overall denoising error. The experiments conducted on representative test video sequences demonstrate that the proposed method provides promising results and is competitive with other state-of-the-art denoising techniques both in terms of objective metric and in perceptual quality.